Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing.

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

SingmiR: a single-cell miRNA alignment and analysis tool.

Single-cell RNA sequencing (RNA-seq) has revolutionized our understanding of cell biology, developmental and pathophysiological molecular processes, paving the way toward novel diagnostic and therapeutic approaches. However, most of the gene regulatory processes on the single-cell level are still unknown, including post-transcriptional control conferred by microRNAs (miRNAs). Like the established single-cell gene expression analysis, advanced computational expertise is required to comprehensively process newly emerging single-cell miRNA-seq datasets. A web server providing a workflow tailored for single-cell miRNA-seq data with a self-explanatory interface is currently not available. Here, we present SingmiR, enabling the rapid (pre-)processing and quantification of human miRNAs from noncoding single-cell samples. It performs read trimming for different library preparation protocols, generates automated quality control reports and provides feature-normalized count files. Numerous standard and advanced analyses such as dimension reduction, clustered feature heatmaps, sample correlation heatmaps and differential expression statistics are implemented. We aim to speed up the prototyping pipeline for biologists developing single-cell miRNA-seq protocols on small to medium-sized datasets. SingmiR is freely available to all users without the need for a login at https://www.ccb.uni-saarland.de/singmir.

Experimental capture of miRNA targetomes: disease-specific 3'UTR library-based miRNA targetomics for Parkinson's disease.

The identification of targetomes remains a challenge given the pleiotropic effect of miRNAs, the limited effects of miRNAs on individual targets, and the sheer number of estimated miRNA-target gene interactions (MTIs), which is around 44,571,700. Currently, targetome identification for single miRNAs relies on computational evidence and functional studies covering smaller numbers of targets. To ensure that the targetome analysis could be experimentally verified by functional assays, we employed a systematic approach and explored the targetomes of four miRNAs (miR-129-5p, miR-129-1-3p, miR-133b, and miR-873-5p) by analyzing 410 predicted target genes, both of which were previously associated with Parkinson's disease (PD). After performing 13,536 transfections, we validated 442 of the 705 putative MTIs (62,7%) through dual luciferase reporter assays. These analyses increased the number of validated MTIs by at least 2.1-fold for miR-133b and by a maximum of 24.3-fold for miR-873-5p. Our study contributes to the experimental capture of miRNA targetomes by addressing i) the ratio of experimentally verified MTIs to predicted MTIs, ii) the sizes of disease-related miRNA targetomes, and iii) the density of MTI networks. A web service to support the analyses on the MTI level is available online ( https://ccb-web.cs.uni-saarland.de/utr-seremato ), and all the data have been added to the miRATBase database ( https://ccb-web.cs.uni-saarland.de/miratbase ).

Characterizing expression changes in noncoding RNAs during aging and heterochronic parabiosis across mouse tissues.

Molecular mechanisms of organismal and cell aging remain incompletely understood. We, therefore, generated a body-wide map of noncoding RNA (ncRNA) expression in aging (16 organs at ten timepoints from 1 to 27 months) and rejuvenated mice. We found molecular aging trajectories are largely tissue-specific except for eight broadly deregulated microRNAs (miRNAs). Their individual abundance mirrors their presence in circulating plasma and extracellular vesicles (EVs) whereas tissue-specific ncRNAs were less present. For miR-29c-3p, we observe the largest correlation with aging in solid organs, plasma and EVs. In mice rejuvenated by heterochronic parabiosis, miR-29c-3p was the most prominent miRNA restored to similar levels found in young liver. miR-29c-3p targets the extracellular matrix and secretion pathways, known to be implicated in aging. We provide a map of organism-wide expression of ncRNAs with aging and rejuvenation and identify a set of broadly deregulated miRNAs, which may function as systemic regulators of aging via plasma and EVs.

Neuro-explicit semantic segmentation of the diffusion cloud chamber.

For decades, in diffusion cloud chambers, different types of subatomic particle tracks from radioactive sources or cosmic radiation had to be identified with the naked eye which limited the amount of data that could be processed. In order to allow these classical particle detectors to enter the digital era, we successfully developed a neuro-explicit artificial intelligence model that, given an image from the cloud chamber, automatically annotates most of the particle tracks visible in the image according to the type of particle or process that created it. To achieve this goal, we combined the attention U-Net neural network architecture with methods that model the shape of the detected particle tracks. Our experiments show that the model effectively detects particle tracks and that the neuro-explicit approach decreases the misclassification rate of rare particles by 73% compared with solely using the attention U-Net.

Ageing-associated small RNA cargo of extracellular vesicles.

Previous work on murine models and humans demonstrated global as well as tissue-specific molecular ageing trajectories of RNAs. Extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different tissues. We sequenced small regulatory RNAs (sncRNAs) in two mouse plasma fractions at five time points across the lifespan from 2-18 months: (1) sncRNAs that are free-circulating (fc-RNA) and (2) sncRNAs bound outside or inside EVs (EV-RNA). Different sncRNA classes exhibit unique ageing patterns that vary between the fcRNA and EV-RNA fractions. While tRNAs showed the highest correlation with ageing in both fractions, rRNAs exhibited inverse correlation trajectories between the EV- and fc-fractions. For miRNAs, the EV-RNA fraction was exceptionally strongly associated with ageing, especially the miR-29 family in adipose tissues. Sequencing of sncRNAs and coding genes in fat tissue of an independent cohort of aged mice up to 27 months highlighted the pivotal role of miR-29a-3p and miR-29b-3p in ageing-related gene regulation that we validated in a third cohort by RT-qPCR.

Dynamic and static circulating cancer microRNA biomarkers - a validation study.

For cancers and other pathologies, early diagnosis remains the most promising path to survival. Profiling of longitudinal cohorts facilitates insights into trajectories of biomarkers. We measured microRNA expression in 240 serum samples from patients with colon, lung, and breast cancer and from cancer-free controls. Each patient provided at least two serum samples, one prior to diagnosis and one following diagnosis. The median time interval between the samples was 11.6 years. Using computational models, we evaluated the circulating profiles of 21 microRNAs. The analysis yielded two sets of biomarkers, static ones that show an absolute difference between certain cancer types and controls and dynamic ones where the level over time provided higher diagnostic information content. In the first group, miR-99a-5p stands out for all three cancer types. In the second group, miR-155-5p allows to predict lung cancers and colon cancers. Classification in samples from cancer and non-cancer patients using gradient boosted trees reached an average accuracy of 79.9%. The results suggest that individual change over time or an absolute value at one time point may predict a disease with high specificity and sensitivity.

isomiRdb: microRNA expression at isoform resolution.

A significant fraction of mature miRNA transcripts carries sequence and/or length variations, termed isomiRs. IsomiRs are differentially abundant in cell types, tissues, body fluids or patients' samples. Not surprisingly, multiple studies describe a physiological and pathophysiological role. Despite their importance, systematically collected and annotated isomiR information available in databases remains limited. We thus developed isomiRdb, a comprehensive resource that compiles miRNA expression data at isomiR resolution from various sources. We processed 42 499 human miRNA-seq datasets (5.9 × 1011 sequencing reads) and consistently analyzed them using miRMaster and sRNAbench. Our database provides online access to the 90 483 most abundant isomiRs (>1 RPM in at least 1% of the samples) from 52 tissues and 188 cell types. Additionally, the full set of over 3 million detected isomiRs is available for download. Our resource can be queried at the sample, miRNA or isomiR level so users can quickly answer common questions about the presence/absence of a particular miRNA/isomiR in tissues of interest. Further, the database facilitates to identify whether a potentially interesting new isoform has been detected before and its frequency. In addition to expression tables, isomiRdb can generate multiple interactive visualisations including violin plots and heatmaps. isomiRdb is free to use and publicly available at: https://www.ccb.uni-saarland.de/isomirdb.

Fast and neat--determination of biochemical quality parameters in cocoa using near infrared spectroscopy.

The qualitative heterogeneity and increasing consumption of cocoa products require fast and efficient methods for quality assessment of fermented cocoa with regard to fermentation quality and flavor potential. To date, quality control is achieved by visual inspection (e.g., "cut test") and sensory testing. Chromatographic methods for quantification of flavor relevant substances are limited in their applicability in standard quality control due to laborious isolation and purification steps. Therefore, the aim of this study was the development of a near infrared spectroscopic (NIRS) method for routine analytical prediction of biochemical quality parameters. Different compound classes like phenolic substances (R(2)=0.93) or organic acids (R(2)=0.88) as well as individual substances like epicatechin (R(2)=0.93) or lactic acid (R(2)=0.87) could be precisely determined just as fermentation time (R(2)=0.92) and pH value (R(2)=0.94) presenting NIRS as fast and reliable alternative in routine quality assessment.